Artificial Intelligence and Big Data Analytics in Vineyards: A Review

*Nathaniel K. Newlands*

## **Abstract**

Advances in remote-sensing, sensor and robotic technology, machine learning, and artificial intelligence (AI) – smart algorithms that learn from patterns in complex data or big data - are rapidly transforming agriculture. This presents huge opportunities for sustainable viticulture, but also many challenges. This chapter provides a state-of-the-art review of the benefits and challenges of AI and big data, highlighting work in this domain being conducted around the world. A way forward, that incorporates the expert knowledge of wine-growers (i.e. human-in-theloop) to augment the decision-making guidance of big data and automated algorithms, is outlined. Future work needs to explore the coupling of expert systems to AI models and algorithms to increase both the usefulness of AI, its benefits, and its ease of implementation across the vitiviniculture value-chain.

**Keywords:** Artificial Intelligence, Big data, Climate change, Decision support, Expert knowledge, Vitiviniculture, Risks

#### **1. Introduction**

Viticulture is at the front line of climate change as grape production is highly sensitive to changing environmental conditions. Growers, producers, and investors plan and anticipate risks far into the future with long time horizons (i.e., 7–11 years or more) for investing, establishing, and attaining positive net income and returns on investment. Growers are grappling with unpredictable, rapidly changing weather patterns and more frequent and intense extreme events such as spring frosts, floods, droughts, heatwaves, and wildfires. Seasonal climate changes of hotter and longer summers and warmer winters are shifting areas suitable for growing grapes further north in the Northern Hemisphere (NH), and south in the Southern Hemisphere (SH), from historical cultivation latitudes of 4° and 51° (NH) and 6° and 45° (SH) [1]. This is driving wine makers to move vineyards to higher elevations that provide colder nighttime temperatures and less frequent and intense peak daytime temperatures to ripen grapes, while preventing over-ripening [2, 3]. Climate change warming scenarios project that grape cultivar diversity may buffer wine-growing regions from losses resulting from both the reduction of suitable areas for growing grapes and attainable yields. In a recent global study using data on long-term French records to extrapolate globally for 11 cultivars (varieties), increasing cultivar diversity more than halved future, projected losses of current wine-growing areas and decreasing areas lost (56 to 24%) under a 2°C warming scenario, and reducing areas lost by a third (85% versus 58%) under a 4°C warming scenario [4]. These warming scenarios combine daily temperature and precipitation from a large ensemble of the Community Earth System Model (CESM), alongside winegrape phenology and global variety-level planting data [5, 6], projecting geographical shifts of areas suitable for grape varieties as well as phenological shifts in the timing of grape ripening (veraison). The resulting loss of suitability of areas is primarily attributed to shifting temperature regimes, and greater accumulations of temperatures above 25°C, and number of days above 40°C. Precipitation was found to have a buffering effect, both reducing the number of varieties that were lost over time, while increasing the capacity for cultivar turnover [4]. While growing diverse cultivars that are more heat-tolerant and drought-resistant can reduce area and yield loss due to climate change impacts, the industry still faces the uncertainty and complexity associated with fulfilling the stringent consumer demands for quality, novelty, cost and sustainability of this agricultural product.

Big data (BD) is data that is machine-readable as opposed to human-readable. There is no official size that makes data "big". It consists of massive amounts of digital information, collected from all sorts of sources that are too large, raw, or unstructured for analysis using conventional relational database and techniques. The internet-of-things (IoT) (i.e., the network of physical objects that exchanging data between devices, software, and systems over the Internet) continues to create BD and expand globally. Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think, learn and problemsolve like humans and mimic their actions. Machine learning (ML) is a sub-set of AI where machines learn from data without being explicitly programmed. Deep learning (DL) is a subset of ML in which artificial neural networks (ANNs) mimics the structure of the human brain, to adapt and learn from vast amounts of data. Algorithms are procedures that are implemented in computer code that use data, and are, in general, distinguished from models, which comprise many algorithms. BD needs to be of sufficient high quality to reliably train, validate, and independently test and/or reproduce algorithmic and model output at reported levels of accuracy and reliability. Here the goal is to design AI algorithms with a fast and efficient learning speed, fast convergence to a solution, good generalization ability and ease of implementation.

### **2. Review objective and methodology**

This review explores the benefits and challenges of BD and AI to sustainable viniviticulture through the lens of recent research findings and insights. Detailing all the different AI methodologies and their implementation is beyond the scope of this review that focuses on their domain application. For background reading of state-of-the-art AI methods and solution techniques, we direct interested readers to an article that features how vineyards are making use of BD [7], a recent introductory methodological reviews of ML in agriculture [8], and DL [9]. In the review conducted and reported here, recently published and highly relevant scientific journal articles were searched and selected using the University of Victoria (UVic)'s Summons 2.0 search engine, which includes a wide range of scientific databases, including the Scopus, ScienceDirect and PubMed databases. A total of 59 articles were selected that met the required, minimal criteria that they assessed, applied, adapted, or developed an AI method/algorithm and addressed a main aspect linked with viniviticulture. This search approach was selective rather than exhaustive or systematic. The resulting sample size is similar to the 40 articles selected as part of another recent AI review which also employed online search of major scientific databases [8].

*Artificial Intelligence and Big Data Analytics in Vineyards: A Review DOI: http://dx.doi.org/10.5772/intechopen.99862*

A systems overview of vitiviniculture interactions and drivers of change was first constructed. This was used to distinguish 10 major aspects under which a range of use-cases could be identified and linked across the selected works. This was informed, in part, by a broad review of vineyard ecosystems, their multifunctionality, and ecosystem services, applied the Common International Classification of Ecosystem Services (CICES) highlights the need to better identify and understand interactions within vineyards, identifying six ecosystem services (or aspects) that are most studied, namely: i) cultivated crops, ii) filtration and sequestration, iii) storage and accumulation, iv) pest and disease control, v) heritage and cultural services, and vi) scientific services (e.g., studying vineyard agronomy) [10]. Challenges identified and described within the selected articles were next extracted, compiled, and synthesized into a summary Table. A depiction or simplified design of a novel BD value chain informed by an ES comprising expert knowledge and providing an ES system with an ability to learn is presented. This is structured to encompass all the identified aspects and potentially capable of addressing current research challenges.
